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Asthma is recognised as one of the major non-communicable chronic respiratory diseases worldwide. The most recent approximate number of people suffering from asthma globally is about 235 million (WHO, 2013). The prevalence rate of this disease is increasing annually (Global Asthma Network, 2014).
This air passage condition remains underdiagnosed, underreported, and undertreated; moreover, the exact causes of asthma are still unknown, but some of the primary contributing factors are chemical pollutants present in the air, as well as indoor and outdoor allergens (WHO, 2013). The cases of asthma caused by occupational factors compose the primary focus of this paper. Critical analysis of several research studies exploring this topic will be carried out to deepen the understanding of mechanisms of research on occupational asthma and its determinants.
When it comes to occupational asthma risks, there exist JEMs or job exposure matrices designed for epidemiological studies and serve as a tool to assess the exposure to various workplace risk factors. Lillienberg, Dahlman-Höglund, Schiöler, Torén, and Andersson (2014) researched older and newer JEMs to find the differences in exposure assignment in them. The authors collected data from a large random sample and processed the data with the help of Cox regression models and Cohen’s kappa. The findings revealed a high degree of agreement between the established and the modified JEMs in the category of “any exposure”, but detected differences in regard to some particular types of exposure.
The primary objective of this study matched the selected sampling technique and sample size; the authors used a large sample (over 13 200 subjects) because the assessment of the large-scale tool such as JEM was required. Since a substantial body of data had to be processed – the authors drew to statistical tools helping find agreement in the two matrices and distinguish the results in men from those in women. One of the major limitations of this study could be the preferred method of data collection – two questionnaires filled in by the subjects.
When dealing with numerical and precise data, such a method can be a weakness due to the difficulty to test whether or not the answers provided by the participants were honest and fair. In order to determine the actual exposure statistics, the findings of this study would have to be compared to those of an expert assessment. Overall, the results of this study supported the initial argument of the authors in regard to the need for frequent updates and modifications of JEMs in order for them to reflect the most relevant and valid results.
Another study focusing on the use of JEMs was that by Kim et al. (2016) there, the authors attempted to identify correlations between the patterns of asthma exacerbation and occupational exposures. Approaching this study critically, it is possible to notice that the size of the researched sample was rather large (over 1300 respondents), which increased the reliability, validity, and generalisability of the findings. Another strong point of this study design is its mechanism of comparison the reported asthma exacerbation rates to workplace exposure according to JEM – a scientifically confirmed set of assigned exposures. This approach made the entire design easy to repeat in a different area and with a different sample. Also, it helped name specific triggers of occupational asthma exacerbation, addressing which could help minimise the problem.
Due to job specificity and specialisation, in many workplaces, occupational asthma is one of the major organisational problems. In contrast with the previous two sources, the study by Jonaid et al. (2015) is focused on the exploration of asthma triggers in a specific industry instead of covering the contributors generally. In particular, the authors investigated the contributors of allergic reaction in the workers of the bakery industry exposed to high molecular weight substances such as different types of flour and enzymes. The authors collected the data from 436 participants (a purposeful sample of bakery workers) and used medical surveillance evaluation in combination with self-administered questionnaires.
The results of the questionnaires were compared to the participants’ medical history. Further, with the help of multivariable logistic regression, the authors could identify the correlations between the occupational contributors to sensitisation and asthma symptoms reflected in medical histories. The authors found that based on the current health status of the workers and their levels of exposure to occupational asthma, they could predict the onset or exacerbation of the condition in the future. In addition, some other studies of with similar objectives used this type of design combining a questionnaire with the research of harmful exposures (Lofstedt, Hagstrom, Bryngelssona, Holmstrom, &Rask-Andersen, 2017; Larcombe, Kicic, & Mullins, 2017).
The authors paid special attention to the achievement of a high level of validity of the results by means of testing and checking their findings. Moreover, the questionnaire responses that were provided by the participants were correlated with the documented medical records of their health status – in this manner, the authors made sure that the answers received from the questionnaire reflected the reality. In that way, it is possible to notice that bias was avoided in a variety of different ways, and this aspect adds to the overall reliability of the results. In addition, even though this study focused on the exploration of asthma triggers in a particular industry, the authors chose an approach that did not depend on the specificity of the workplace. As a result, despite its narrow focus, this study can be repeated in another industry and in a different workplace.
In order to deepen the scientific knowledge of a certain issue, it could be researched on a large scale using cohort studies or systematic reviews; and also, the analysis of a single case could help expand what is known about the problem. The latter approach as employed by Vandenplas et al. (2008) in their study if a single case of a worker whose asthma and rhinoconjunctivitis symptoms were caused by his long-term exposure to herbal (chamomile) dust at a tea packing factory. The individual was tested using deliberate exposure to black and chamomile tea particles. His reactions to the substances were measured in the clinical environment, and the conclusion was made that the aforementioned worker did, in fact, develop his symptoms due to working with chamomile tea for 11 years.
The results of this study are very specific and are limited to the reaction and symptoms of a single subject. The authors mentioned that, to their knowledge, this report of asthma symptoms was the first in the history of medicine that found chamomile to be the trigger. Despite the extremely narrow specification, this study as a significant scientific weight as the pioneer in the sphere of studying and documenting a potentially new set of asthma triggers that have not been identified previously. Of course, more reports linking the symptoms of asthma and other respiratory conditions with tea-packing occupations are needed for a full-scale investigation to begin in this industry.
In contrast with the previous research that focused on the industry that is not known as a typical source of occupational asthma, the research by Baldi et al. (2014) investigated the risk of respiratory conditions in agricultural specialisations – a field with a high rate of occupational asthma prevalence. A wide variety of factors correlated with the presence and absence of allergy were assessed in a purposeful sample of 1246 asthmatic employees. Multivariate analysis of the data collected from the participants’ occupations and medical histories found a series of factors typical to allergic and non-allergic subjects.
Assessing this study, it is necessary to notice the large size of its sample, adding to the generalisability of findings. Also, the authors used an approach that can be repeated in a different area for the purpose of expanding the known body of information. Due to the large amounts of numerical data, the authors chose to use multivariate analysis in order to detect patterns. In order to elaborate on their findings, the authors also included the demographics of their participants so that it was possible to match the findings to the socioeconomic and biological factors and notice potential correlations.
Conclusion
The critical analysis of several scientific studies focusing on the exploration of occupational asthma and its triggers revealed two major approach – the ones focusing on the problem in general, and thus working with large samples for higher representability and generalizability and the ones choosing to investigate the problem of occupational asthma in a narrower setting of a particular industry; the authors of latter kind of articles work with a specific set of triggers and a purposeful sample.
In addition, the approach has a case study design and focuses on an individual scenario. Though rare, this approach is highly valuable for the pioneering studies opening a new field of research and new potential triggers. Moreover, large-scale studies use questionnaires to assess workers exposures to asthma triggers. The use of this data collection method should be combined with a more reliable data mining method since questionnaires alone allow too much potential bias to serve as the sources of quantitative data.
References
Baldi, I., Robert, C., Piantoni, F., Tual, S., Bouvier, G., Lebailly, P., & Raherison, C. (2014). Agricultural exposure and asthma risk in the AGRICAN French cohort. International Journal of Hygiene and Environmental Health, 217(4-5), 435-442. Web.
Global Asthma Network. (2014). The global asthma report 2014. Web.
Jonaid, B., Rooyackers, J., Stigter, E., Portengen, L., Krop, E., & Heederik, D. (2015). Predicting occupational asthma and rhinitis in bakery workers referred for clinical evaluation. Occupational and Environmental Medicine, 0, 1-9. Web.
Kim, J., Henneberger, P., Lohman, S., Olin, A., Dahlman-Höglund, A., Andersson, E., … Holm, M. (2016). Impact of occupational exposures on exacerbation of asthma: a population-based asthma cohort study. BMC Pulmonary Medicine, 16(1). Web.
Larcombe, A., Kicic, A., & Mullins, B. (2017). Comment on “long-term effects of diesel exhaust particles on airway inflammation and remodeling in a mouse model” by Kim et al. Allergy, Asthma & Immunology Research, 9(2), 185. Web.
Lillienberg, L., Dahlman-Hoglund, A., Schioler, L., Toren, K., & Andersson, E. (2014). Exposures and asthma outcomes using two different job exposure matrices in a general population study in Northern Europe. Annals of Occupational Hygiene, 58(4), 469-481. Web.
Lofstedt, H., Hagstrom, K., Bryngelssona, I., Holmstrom, M., & Rask-Andersen, A. (2017). Respiratory symptoms and lung function in relation to wood dust and monoterpene exposure in the wood pellet industry. Upsala Journal of Medical Sciences, 1-8. Web.
Vandenplas, O., Pirson, F., D’Alpaos, V., Vander Borght, T., Thimpont, J., & Pilette, C. (2008). Occupational asthma caused by chamomile. Allergy, 63(8), 1090-1092. Web.
WHO. (2013). Asthma. Web.
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